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Introduction

Ferroptosis, first defined by Dixon et al (1) in 2012, is a relatively novel type of programmed cell death distinguished from apoptosis, necroptosis and pyroptosis by the presence of smaller mitochondria and a reduced number of mitochondrial cristae. Ferroptosis was initially explored in mammalian systems and is now known as one of the most prevalent forms of cell death (2). Ferroptosis is driven by lethal lipid peroxidation resulting from unbalanced redox homeostasis and cellular metabolism, which is inhibited by antioxidant systems, mainly including the glutathione peroxidase 4 (GPX4)-glutathione (3), ferroptosis suppressor protein 1 (FSP1)-CoQH2 (4,5), GTP cyclohydrolase 1 (GCH1)-tetrahydrobiopterin (6) and dihydroorotate dehydrogenase (DHODH)-CoQH2 systems (7). RAS-selective lethal 3 (RSL3) and Erastin are two small-molecule RSL compounds that induce ferroptosis, which is inhibited by ferrostatin-1 (Fer-1). RSL3 directly inactivates GPX4, inhibiting its ability to convert lipid hydroperoxides to lipid alcohols and triggering ferroptosis, whereas Erastin suppresses system Xc-, decreasing cystine uptake and enhancing the levels of reactive oxygen species (ROS) (1,8,9). Fer-1 is thought to have antioxidant abilities as it scavenges alkoxyl radicals and other rearrangement products generated by ferrous iron from lipid hydroperoxides (10). Dixon et al (1) found that Fer-1 specifically inhibited ferroptosis induced by RSL but not the other known forms of cell death. Various signalling pathways, such as the p53 and PI3K/AKT/mTOR signalling pathways, linked to cancer regulate ferroptosis in cancer cells, and accumulating evidence suggests that ferroptosis has an essential role in cancer progression (11,12).

Long non-coding RNAs (lncRNAs), defined as non-coding transcripts with sequences >200 nucleotides, are involved in gene expression regulation in diverse biological processes, including as RNA decoys (13), microRNA (miRNA) sponges (14) and scaffolds (15). lncRNAs have important roles as tumour suppressors or oncogenes, influencing the emergence of cancer (16). Recently, RNA sequencing (RNA-Seq) and bioinformatics analyses have demonstrated the involvement of certain lncRNAs in ferroptosis. For instance, H19 has an important role in ferroptosis caused by curcumenol by regulating miR-19b-3p-mediated ferritin heavy chain 1 (FTH1) downregulation (17). Moreover, Qin et al (18) constructed a 5-lncRNA (AC055720.2, LINC00900, DPP4-DT, LINC02454 and AC012038.2) signature related to ferroptosis to predict thyroid cancer prognosis and immune responses. Nevertheless, the function and regulatory mechanisms of lncRNAs associated with ferroptosis in cancer have not yet been fully investigated.

Lung cancer-associated transcript 1 (LUCAT1) was first identified in the airways of patients with a history of smoking; hence, it is sometimes referred to as smoke and cancer-associated lncRNA-1 (19). LUCAT1 expression is essential for tumour-node-metastasis staging and overall survival predictions for several malignancies, including liver cancer (20), gastric cancer (21), colorectal cancer (22,23) and renal cell carcinoma (24). Previous bioinformatics data have indicated that LUCAT1 is a ferroptosis-related lncRNA that can be used in prognostic or diagnostic signatures in renal cell carcinoma (25,26) and glioma (27). However, to the best of our knowledge, the role of LUCAT1 in lung cancer ferroptosis and its underlying mechanisms remain unknown.

Therefore, the aim of the present study was to identify ferroptosis-related lncRNAs and investigate their roles and regulatory mechanisms in lung cancer. Lung cancer cells were treated with the ferroptosis inducer, RSL3, and inhibitor, Fer-1, and ferroptosis-associated lncRNAs were identified by RNA-Seq. Investigating the functions of these lncRNAs in ferroptosis offers a novel theoretical foundation for therapeutic strategies in lung cancer.

Materials and methods

Cell culture

The human normal lung epithelial cell line, BEAS-2B (cat. no. HTX2075), was obtained from Otwo Biotech (Shenzhen Haodihuatuo Biotechnology Co., Ltd.). The lung cancer cell line, A549 (cat. no. CCL-185), and the 293T cell line (cat. no. CRL-11268) were obtained from the American Type Culture Collection. The lung cancer cell line, H460 (cat. no. CL-0299), was obtained from Procell Life Science & Technology Co., Ltd. All cell lines were authenticated by their suppliers through STR profiling. The A549, BEAS-2B, and 293T cells were cultured in DMEM (Gibco; Thermo Fisher Scientific, Inc.) with 10% foetal bovine serum (Gemini Bio Products), 100 U/ml penicillin and 100 μg/ml streptomycin at 37°C with 5% CO2. H460 cells were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) with the same supplements and conditions.

RNA-Seq

A549 cells were treated in triplicate with 0.7% dimethyl sulfoxide (DMSO; solvent used to dissolve RSL3 and Fer-1), 2 μM RSL3 (28) (cat. no. HY-100218A; MedChemExpress), or 2 μM RSL3 and 5 μM Fer-1 (29) (cat. no. S7243; Selleck Chemicals) for 24 h. Total RNA was extracted using TRIzol reagent (cat. no. T9424; Sigma-Aldrich; Merck KGaA), and the quantity and purity of the extracted RNA were analysed using an RNA 6000 Nano LabChip Kit (cat. no. 5067-1511; Agilent Technologies, Inc.) by Bioanalyzer 2100 (Agilent Technologies, Inc.). The 2x 150 bp paired-end sequencing was performed using an Illumina Novaseq™ 6000 at LC-Bio Technology (Hangzhou) Co., Ltd.

Bioinformatics analysis

Using genes from the aforementioned 9 samples with a mean fragments per kilobase of transcript per million mapped reads value >1, differentially expressed genes (DEGs) between the treatment and control samples were identified using DEseq2 software (version 1.40.2; https://github.com/mikelove/DESeq2), based on fold change >1.5 and P<0.05 as the cut-off. Biological Process (BP) of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the DEGs were performed using Metascape (https://metascape.org/gp) (30). Heatmaps, volcano plots and Venn diagrams were created using OmicStudio (https://www.omicstudio.cn). Protein-protein interaction (PPI) networks were examined using STRING (https://www.string-db.org/). The co-expression analysis of mRNA-lncRNA was performed using the online resource, weishengxin (https://www.bioinformatics.com.cn). GO analysis of LUCAT1 and the prediction of target genes of DElncRNAs was performed using LncACTdb 3.0 (http://bio-bigdata.hrbmu.edu.cn/LncACTdb) (31). Expression specificity analysis of mRNA and lncRNA was conducted using index τ, following the method described by Li et al (32). The location of LUCAT1 was predicted using lncLocator (http://www.csbio.sjtu.edu.cn/bioinf/lncLocator2/) (33). LUCAT1 data were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) [accession nos. GSE104462 (34) and GSE158458 (35)] and were associated with liver cancer lines (including HepG2, Hep3B and Huh7). Potential miRNAs targeting LUCAT1 or the 3'-untranslated region (3'-UTR) of GCH1 were predicted using DIANA-LncBase v3 (https://diana.e-ce.uth.gr/lncbasev3/interactions) (36) or TargetScan (http://www.targetscan.org). miR-34a-5p and LUCAT1 or GCH1 binding sites were predicted using miRDB (http://www.mirdb.org) (37) or TargetScan. Pan-cancer data were downloaded from the UCSC Xena Pan-Cancer Atlas Hub (https://xenabrowser.net/datapages/), and the expression of lncRNAs were analysed using R software (version 4.2.2; https://cran.r-project.org/src/base/R-4/).

Plasmid, stable cell line generation, small interfering RNA (siRNA), miRNA mimics and miRNA inhibitors transfections

The LUCAT1-expression vector was constructed using the PB-CMV-MCS-EF1α-RedPuro vector (cat. no. PB514B-2; System Biosciences, LLC) by YouBio. A549 cells in 6-well plates were co-transfected with 0.5 μg LUCAT1-expression vector (LUCAT1) or its control (Vector) and 0.2 μg Super PiggyBac Transposase Expression Vector (cat. no. PB210PA-1; System Biosciences, LLC) using the jetPRIME transfection reagent (cat. no. 101000046; Polyplus-transfection SA) at room temperature, and cells that stably expressed LUCAT1 were selected using 2 μg/ml puromycin (cat. no. A1113803; Gibco; Thermo Fisher Scientific, Inc.) and subsequently maintained with 1 μg/ml puromycin. LUCAT1 siRNA (siLUCAT1) and its control (siNC), mimics of miR-34a-5p (miR-34a-5p) and its control (miR-NC), and inhibitors of miR-34a-5p (anti-miR-34a-5p) and its control (anti-NC), synthesised by Genepharm, Inc., were transfected into H460 cells at a final concentration of 50 nM under the same conditions as aforementioned. The sequences used in these experiments are shown in Table SI. The gene RNA expression level was detected 48 h after transfection, and the protein expression level was detected 72 h after transfection.

Cell viability assay

The viability of cells was assessed using Cell Counting Kit-8 (CCK-8; cat. no. CK04; Dojindo Molecular Technologies, Inc.). To measure cell proliferation, cells were seeded at a density of 1×103 cells/well into 96-well plates and cultured for 0, 24, 48 and 72 h. To measure cytotoxicity, cells were seeded at a density of 5×103 cells/well into 96-well plates, cultured overnight and treated with RSL3 and Erastin (A549 cells were treated with 2 μM RSL3 or 20 μM Erastin and H460 cells were treated with 8 μM RSL3 or 20 μM Erastin) for 24 h. After adding the mixture (CCK-8 reagent: medium, 1:10) into the wells and incubating at 37°C for 1 h, a microplate reader (LabSystems Multiskan MS; Thermo Fisher Scientific, Inc.) was used to measure the absorbance at 450 nm.

Colony formation assay

Cells were seeded at a density of 1×103 cells/well into 6-well plates, cultured overnight and treated with RSL3 or Erastin (A549 cells were treated with 0.1 μM RSL3 or 4 μM Erastin and H460 cells were treated with 2 μM RSL3 or 4 μM Erastin) for 10 days. After 20 min of methanol fixation at room temperature, 0.2% crystal violet was used to stain the cells for 5 min at room temperature. ImageJ software (version 1.53a; National Institutes of Health) was used to count colonies with >50 cells, and analysis was performed using GraphPad Prism (version 9.0; Dotmatics).

Lipid ROS assay

Lipid ROS was detected using a BODIPY 581/591 C11 Kit (cat. no. D3861; Invitrogen; Thermo Fisher Scientific, Inc.). Cells were seeded at a density of 1.5×105 cells/well into 6-well plates, cultured overnight and treated with RSL3 or Erastin (A549 cells were treated with 2 μM RSL3 or 20 μM Erastin and H460 cells were treated with 8 μM RSL3 or 20 μM Erastin) at 37°C for 24 h. Subsequently, the cells were harvested and stained with 2 μM C11-BODIPY (581/591) probe at 37°C for 30 min in the dark. At least 1×104 cells were collected to measure the fluorescence using the BD FACSAria™ II flow cytometer (BD Biosciences). FlowJo V10 software (FlowJo, LLC) was used to analyse the data.

Reverse transcription-quantitative PCR (RT-qPCR)

TRIzol reagent was used to extract the total RNA, and the extracted RNA was reverse transcribed into cDNA using an ImProm-II™ Reverse Transcriptase Reagent Kit (cat. no. A3803; Promega Corporation) according to the manufacturer's instructions. qPCR was performed in a Mx3000P instrument (Agilent Technologies, Inc.) using PowerUp™ SYBR™ Green Master Mix (cat. no. A24742; Applied Biosystems; Thermo Fisher Scientific, Inc.). For detecting miRNA expression, poly(A) tails were added before RT using E. coli poly(A) polymerase (cat. no. M0276S; New England BioLabs, Inc.). The qPCR conditions were as follows: i) 50°C for 2 min; ii) 95°C for 2 min; and iii) 40 cycles of 95°C for 5 sec and 60°C for 30 sec. To normalise the levels of miRNA expression and other genes, U6 or glyceraldehyde-3-phosphate dehydrogenase (GAPDH) RNA were utilised, and the relative gene expression levels were calculated using the 2−ΔΔCq method (38). Table SII depicts the RT-qPCR primer sequences used in this assay.

Western blotting

RIPA Lysis Buffer (cat. no. ZE122-101S; Genstar Biosolutions Co., Ltd.) containing Protease Inhibitor Cocktail (cat. no. B14001; Bimake) was used to extract the total proteins from cells. The Pierce™ BCA Protein Assay Kit (cat. no. 23227; Thermo Fisher Scientific, Inc.) was utilised to quantify protein concentrations in the samples. Then, samples with 15 μg of protein were separated via 12% SDS-PAGE gels and transferred to nitrocellulose membranes (cat. no. 66485; Pall Life Sciences). After blocking the membrane with 5% non-fat milk at room temperature for 1 h, the membranes were incubated with primary antibodies overnight at 4°C. The following primary antibodies were used: GAPDH (1:20,000; cat. no. 60004-1-Ig; ProteinTech Group, Inc.); GCH1 (1:2,000; cat. no. 28501-1-AP; ProteinTech Group, Inc.); GPX4 (1:5,000; cat. no. ab125066; Abcam) and FSP1 (1:2,000; cat. no. 20886-1-AP; ProteinTech Group, Inc.). Then, the membranes were incubated with secondary antibodies at room temperature for 1 h. The secondary antibodies used were as follows: Goat anti-mouse (1:5,000; cat. no. SA00001-1; ProteinTech Group, Inc.) and goat anti-rabbit (1:5,000; cat. no. SA00001-2; ProteinTech Group, Inc.). The signals were visualised using HRP Substrate Luminol Reagent (cat. no. WBKLS0500; MilliporeSigma) and imaged using a chemiluminescence imaging system (Tanon-4600SF; Tanon Science and Technology Co., Ltd.). ImageJ software was used for the semi-quantitation of protein.

Dual-luciferase reporter assay

Full-length LUCAT1 was inserted into the pGL3-basic vector (Promega Corporation) using XbaI and NdeI (cat. nos. R0145S and R0111S; New England BioLabs, Inc.) to create the LUCAT1 reporter vector. The GCH1 3'-UTR reporter vector was also constructed by insertion into the pGL3-basic vector. Using the QuickMutation™ Plus Kit (cat. no. D0208S; Beyotime Institute of Biotechnology), the binding sites 'CACUGCC' (position 869-875 in LUCAT1 or position 255-261 in the GCH1 3'-UTR) were changed to 'GCGACGG' to create mutated variants of the LUCAT1 and the GCH1 3'-UTR reporter vector. Dual-luciferase reporter assays were conducted by co-transfecting 293T cells with the Renilla Luciferase Expression Vector (Promega Corporation), the LUCAT1 or GCH1 3'-UTR reporter vector, and miR-NC or miR-34a-5p mimics using jetPRIME transfection reagent. After 48 h of transfection, the cells were lysed by the Dual-Luciferase Reporter Assay System (cat. no. E1910; Promega Corporation), and the luciferase activities were measured by GloMax® 20/20 Luminometer (Promega Corporation). Transfection efficiencies were standardised using Renilla luciferase. Table SIII shows the sequences of the primers used in this assay.

Statistical analysis

The data are presented as the mean ± SD. Unpaired Student's t-test (two-tailed) and one-way analysis of variance (ANOVA) or two-way ANOVA followed by Tukey's test were used to analyse the differences between the different groups, using GraphPad Prism software. P<0.05 was used to indicate a statistically significant difference.

Results

Identification of DEmRNAs involved in regulating ferroptosis

To identify genes involved in ferroptosis, A549 cells were treated with DMSO, RSL3 or RSL3 + Fer-1. RSL3 (2 μM) inhibited approximately half of the cell viability of A549 cells, which was prevented by co-incubation with 5 μM Fer-1 (Fig. S1). Subsequently, RNA-Seq was performed. When comparing the RSL3 treatment group with the DMSO group, 291 DEmRNAs were identified, including the ferroptosis-related genes, solute carrier family 7 member 11, prostaglandin-endoperoxide synthase 2, FTH1, heme oxygenase 1 and TP53 (1,39-42) (Fig. 1A and B), of which 82 were rescued (expression level was restored) by co-incubation with Fer-1, indicating that these 82 DEmRNAs may be important for ferroptosis (Fig. 1C). The DEmRNAs were enriched in the BP terms, 'response to oxygen levels' and 'positive control of programmed cell death' (Fig. 1D). KEGG pathway analysis revealed that the ferroptosis-related, 'HIF-1 signaling pathway' (43), and 'TNF signaling pathway' (44) were also enriched during ferroptosis (Fig. 1E). Among the top 10 hub mRNAs in the PPI network displayed using STRING, jun proto-oncogene, C-X-C motif chemokine ligand 8, epiregulin, cyclin-dependent kinase inhibitor 1A, serpin family E member 1 and DNA damage-inducible transcript 3 were previously reported to be involved in ferroptosis (45-50), suggesting that the remaining four genes, growth arrest and DNA damage inducible α (GADD45A), epithelial mitogen (EPGN), heparin-binding EGF-like growth factor (HBEGF) and transforming growth factor α (TGFA), may also have roles in ferroptosis (Fig. 1F and G). These results therefore identified novel genes that may be involved in ferroptosis caused by alkoxyl radicals and other rearrangement products generated by ferrous iron from lipid hydroperoxides.

Figure 1 Expression profiles of mRNAs in ferroptosis. (A) Volcano plot for the RSL3 group vs. the DMSO group. (B) The FPKM value of ferroptosis-associated genes in the DMSO and RSL3 groups. (C) Heatmap showing the overlap of 82 DEmRNAs between the RSL3 vs. DMSO groups and the RSL3 vs. RSL3 + Fer-1 groups. (D) Top 20 GO-BP terms for the DEmRNAs. (E) KEGG terms for the DEmRNAs. (F) PPI network of DEmRNAs. (G) The FPKM value of the top 10 hub genes in the PPI network. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. RSL3, RAS-selective lethal 3; DEmRNAs, differentially expressed mRNAs; Fer-1, ferrostatin-1; GO, Gene Ontology; BP, Biological Process; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI, protein-protein interaction; FPKM, fragments per kilobase of transcript per million mapped reads; FC, fold change; ns, not significant; SLC7A11, solute carrier family 7 member 11; FTH1, ferritin heavy chain 1; PTGS2, prostaglandin-endoperoxide synthase 2; HMOX1, heme oxygenase 1; JUN, jun proto-oncogene; CXCL8, C-X-C motif chemokine ligand 8; EREG, epiregulin; CDKN1A, cyclin-dependent kinase inhibitor 1A; DDIT3, DNA damage-inducible transcript 3; GADD45A, growth arrest and DNA damage inducible α; HBEGF, heparin-binding EGF-like growth factor.

LUCAT1 is upregulated in RSL3-induced ferroptosis, which is prevented by co-incubation with Fer-1

In addition to the 82 DEmRNAs, 18 DElncRNAs were either upregulated or downregulated in the RSL3 group compared with the DMSO group, which was prevented by co-incubation with Fer-1 (Fig. 2A-D). The genes targeted by DElncRNAs were enriched in the BP terms 'regulation of growth' and 'response to oxygen levels' (Fig. 2E). Additionally, KEGG pathway analysis revealed that the 'MAPK signaling pathway' (51,52), 'Hippo signaling pathway' (53) and 'Wnt signaling pathway' (54) were enriched (Fig. 2F), suggesting that DElncRNAs are involved in ferroptosis.

Figure 2 LUCAT1 is upregulated in RSL3-induced ferroptosis, which is inhibited by Fer-1. (A) Volcano map for the RSL3 vs. DMSO group. (B) Volcano map for the RSL3 vs. RSL3 + Fer-1 group. (C) The overlap of the DElncRNAs between the RSL3 vs. DMSO group and the RSL3 vs. RSL3 + Fer-1 group. (D) Heatmap of the 18 DE lncRNAs. (E) Top 20 GO-BP terms enriched for the target genes of DElncRNAs. (F) KEGG terms enriched for the target genes of the DElncRNAs. (G) Log2 (FPKM+1) of lncRNAs and mRNAs. (H) Specific expression analysis of lncRNAs and mRNAs. (I) Co-expression networks between DElncRNAs and DEmRNAs. (J) DElncRNAs ranked in terms of their log2 FC values. (K) The FPKM value for LUCAT1 was determined using RNA sequencing. (L) LUCAT1 expression in the DMSO, RSL3 and RSL3 + Fer-1 groups was detected using RT-qPCR. (M) LUCAT1 expression in A549 cells treated with 0, 1, 2 or 4 μM RSL3 for 24 h was detected using RT-qPCR. (N) LUCAT1 expression in A549 cells treated with 4 μM RSL3 for 0, 12, 24 or 48 h was detected using RT-qPCR. (O) GO analysis of LUCAT1 was predicted using LncACTdb. **P<0.01, ***P<0.001, ****P<0.0001. All experiments were repeated three times. LUCAT1, lung cancer-associated transcript 1; RSL3, RAS-selective lethal 3; Fer-1, ferrostatin-1; DE, differentially expressed; lncRNA, long non-coding RNA; GO, Gene Ontology; BP, Biological Process; KEGG, Kyoto Encyclopedia of Genes and Genomes; FC, fold change; FPKM, fragments per kilobase of transcript per million mapped reads; RT-qPCR, reverse transcription-quantitative PCR.

Compared with mRNA expression levels, the global expression level of lncRNA was much lower (Fig. 2G). However, the lncRNAs displayed a greater degree of expression specificity compared with that of the mRNAs (Fig. 2H). To identify lncRNAs that were more important in ferroptosis, their correlations with DEmRNAs were examined and mRNA-lncRNA co-expression networks were constructed. Using a robust correlation (|r|>0.9, P<0.05) as the cut-off for mRNA-lncRNA pairs, 93 nodes (16 DElncRNAs and 77 DEmRNAs) and 589 mRNA-lncRNA pairs (544 with a positive correlation and 45 with a negative correlation) were identified. The mRNA-lncRNA pairs with the top 100 correlation coefficients (|r|) were used for network construction (Fig. 2I).

Among the 18 DElncRNAs, LUCAT1 was the most highly upregulated, ranking first among the candidates (Fig. 2J), and it had an important role in the constructed mRNA-lncRNA co-expression network (Fig. 2I). Therefore, it was next confirmed that LUCAT1 was upregulated following treatment with RSL3 and prevented by co-incubation with Fer-1 (Fig. 2K and L). After treating A549 cells with RSL3, LUCAT1 expression increased in a dose-dependent manner and maximum LUCAT1 expression occurred after 12 h of treatment with 4 μM RSL3 (Fig. 2M and N). To obtain more evidence for the connection between LUCAT1 and ferroptosis, the GEO database was utilised to explore the relationship between LUCAT1 and other ferroptosis inducers in cancer. It was found that LUCAT1 was elevated in HepG2 liver cancer cells treated with Erastin and in two sorafenib-resistant liver cancer lines (Hep3B and Huh7) by bioinformatic analysis (Fig. S2A-C), suggesting that ferroptosis inducer-induced upregulation of LUCAT1 may be common in different types of cancer. Furthermore, the top three GO terms for LUCAT1 predicted by the LncACTdb were 'superoxide metabolic process' (55), 'MAPK activity' (51,52) and 'oxygen and reactive oxygen species metabolic process' (56), which all have important roles in ferroptosis (Fig. 2O). These results reflected a possible close relationship between ferroptosis and LUCAT1.

Genes that are expressed at either high or low levels in tumours influence the growth and treatment of the tumours (57). LUCAT1 expression was higher in 18 different types of cancer tissues than in the corresponding normal tissues (Fig. S3A). Compared with normal tissues, LUCAT1 expression was higher in lung adenocarcinoma and lung squamous cell carcinoma (Fig. S3B and C). Similarly, two lung cancer cell lines, A549 and H460, expressed higher levels of LUCAT1 compared with the BEAS-2B normal lung epithelial cell line (Fig. S3D). These findings suggested that LUCAT1 functions as an oncogene in lung cancer.

LUCAT1 overexpression inhibits RSL3- and Erastin-induced ferroptosis

The expression of LUCAT1 was lower in A549 cells than in H460 cells (Fig. S3D); therefore, the effect of overexpressing LUCAT1 in A549 cells was examined (Fig. 3A). As shown in Fig. 3B, LUCAT1 overexpression significantly enhanced A549 cell proliferation. When ferroptosis was induced by RSL3 and Erastin, overexpression of LUCAT1 enhanced the survival rate and colony formation of A549 cells (Fig. 3C and D). Similarly, lipid peroxidation levels were elevated by RSL3 and Erastin treatment; however, LUCAT1 overexpression reduced these levels (Fig. 3E). Collectively, these data suggested that LUCAT1 was a ferroptosis suppressor in A549 cells treated with RSL3 and Erastin.

Figure 3 LUCAT1 overexpression inhibits RSL3- and Erastin-induced ferroptosis. (A) LUCAT1 expression in A549 cells was detected using reverse transcription-quantitative PCR. (B) The effect of LUCAT1 overexpression on cell proliferation was detected by CCK-8. (C) A549 cells were treated with 2 μM RSL3 or 20 μM Erastin for 24 h and the cell viability was determined using CCK-8. (D) Colony formation assay was performed following treatment with 0.1 μM RSL3 or 4 μM Erastin. (E) The histograms of lipid ROS for A549 cells treated with 2 μM RSL3 or 20 μM Erastin for 24 h. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. All experiments were repeated three times. LUCAT1, lung cancer-associated transcript 1; RSL3, RAS-selective lethal 3; CCK-8, Cell Counting Kit-8; ns, not significant; ROS, reactive oxygen species.

Knocking down LUCAT1 expression promotes RSL3- and Erastin-induced ferroptosis

As LUCAT1 was expressed at higher levels in H460 cells than in A549 cells (Fig. S3D), the effect of knocking down LUCAT1 expression in H460 cells was next explored (Fig. 4A). Knocking down LUCAT1 expression significantly inhibited the proliferation of H460 cells (Fig. 4B). Knocking down LUCAT1 expression also significantly reduced the survival rate and colony formation capacity of H460 cells following ferroptosis induction (Fig. 4C and D). Moreover, knocking down LUCAT1 expression increased the level of lipid peroxidation upon ferroptosis induction (Fig. 4E). These data suggested that knocking down LUCAT1 expression promoted RSL3- and Erastin-induced ferroptosis.

Figure 4 LUCAT1 knockdown promotes RSL3- and Erastin-induced ferroptosis. (A) LUCAT1 expression in H460 cells was detected using reverse transcription-quantitative PCR. (B) The effect of LUCAT1 knockdown on cell proliferation was detected by CCK-8. (C) H460 cells were treated with 8 μM RSL3 or 20 μM Erastin for 24 h and the cell viability was determined by CCK-8. (D) Colony formation assay was performed following treatment with 2 μM RSL3 or 4 μM Erastin. (E) The histograms of lipid ROS for H460 cells treated with 8 μM RSL3 or 20 μM Erastin for 24 h. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. All experiments were repeated three times. LUCAT1, lung cancer-associated transcript 1; RSL3, RAS-selective lethal 3; CCK-8, Cell Counting Kit-8; ns, not significant; ROS, reactive oxygen species; si(RNA), small interfering RNA; NC, negative control.

Knocking down LUCAT1 expression promotes ferroptosis by regulating the miR-34a-5p/GCH1 axis

To investigate the mechanism by which LUCAT1 influences ferroptosis, the expression of the main ferroptosis resistance genes were detected. FSP1 and GCH1 were downregulated in LUCAT1-knock down cells, while GPX4 expression was not affected (Fig. 5A). Owing to the greater downregulation of GCH1, we considered that LUCAT1 primarily exerted its effects through GCH1. LncLocator analysis indicated that LUCAT1 is predominantly localised in the cytoplasm (Fig. 5B); thus, it may act as a miRNA sponge in regulating GCH1 expression. Next, the shared miRNAs targeting the 3'-UTR of GCH1 (predicted by TargetScan) and LUCAT1 (predicted by DIANA-LncBase v3) were analysed and miR-34a-5p was identified as a potential mediator between the two (Fig. 5C). As illustrated in Fig. 5D, the expression level of miR-34a-5p was elevated upon LUCAT1 knockdown. Moreover, transfection with miR-34a-5p mimics downregulated GCH1 expression (Fig. 5E and F). Additionally, miR-34a-5p mimics enhanced the RSL3-induced ferroptosis sensitivity as determined via cell viability and lipid ROS assays (Fig. 5G and H). Conversely, inhibition of miR-34a-5p with anti-miR-34a-5p increased the resistance of ferroptosis mediated by LUCAT1 inhibition (Fig. 5I and J).

Figure 5 Knocking down LUCAT1 expression promotes ferroptosis by regulating the miR-34a-5p/GCH1 axis. (A) Expression of ferroptosis-associated proteins in H460 cells after knocking down LUCAT1 expression was detected using western blotting. (B) The subcellular localisation of LUCAT1 was predicted using lncLocator. (C) Venn diagram of overlapping miRNAs related to LUCAT1 and the 3'-UTR of GCH1. (D) miR-34a-5p expression in H460 cells after knocking down LUCAT1 expression was detected using RT-qPCR. (E) miR-34a-5p expression in H460 cells transfected with miR-34a-5p mimics was detected using RT-qPCR. (F) GCH1 protein expression in H460 cells transfected with miR-34a-5p mimics was detected using western blotting. (G) H460 cells were treated with 8 μM RSL3 for 24 h and the cell viability was determined by using CCK-8. (H) The lipid ROS histograms for H460 cells treated with 8 μM RSL3 for 24 h. (I) miR-34a-5p expression in H460 cells transfected with miR-34a-5p inhibitors was detected using RT-qPCR. (J) The viability of H460 cells treated with siNC, siLUCAT1-1, siLUCAT1-1 + anti-NC or siLUCAT1-1 + anti-miR-34a-5p groups was assessed following treatment with 8 μM RSL3 or 20 μM Erastin for 24 h using CCK-8. (K) The miR-34a-5p binding sites of LUCAT1 and 3'-UTR of GCH1 were predicted using miRDB and TargetScan. (L) Dual-luciferase reporter assays were performed to determine the associations between miR-34a-5p and LUCAT1 or GCH1. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. All experiments were repeated three times. LUCAT1, lung cancer-associated transcript 1; GCH1, GTP cyclohydrolase 1; 3'-UTR, 3'-untranslated region; FSP1, ferroptosis suppressor protein 1; GPX4, glutathione peroxidase 4; CCK-8, Cell Counting Kit-8; RT-qPCR, reverse transcription-quantitative PCR; ns, not significant; ROS, reactive oxygen species; si(RNA), small interfering RNA; NC, negative control; miR, microRNA; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; WT, wild-type; MUT, mutated.

Using miRDB and TargetScan, the potential miR-34a-5p binding sites with LUCAT1 and the 3'-UTR of GCH1 were identified (Fig. 5K). The potential binding sites between miR-34a-5p and LUCAT1 or the 3'-UTR of GCH1 were then validated using dual-luciferase reporter assays. The luciferase activity was markedly lower in 293T cells transfected with miR-34a-5p mimics than with miR-NC; however, no changes in luciferase activity were observed in the LUCAT1-mutated (MUT) or GCH1-MUT groups (Fig. 5L). In conclusion, these findings suggested that LUCAT1 may promote ferroptosis via the miR-34a-5p/GCH1 axis.

Discussion

As ferroptosis is a targetable cancer vulnerability, more attention has been paid to the impact of ferroptosis-associated genes in recent years (12). Therefore, lncRNA expression profiles were analysed in the present study and it was discovered that LUCAT1 expression was elevated following treatment with RSL3, which was prevented by Fer-1. The results of the present study also suggested that LUCAT1 acted as a miR-34a-5p sponge and upregulated GCH1 expression, enhancing the resistance of lung cancer cells to ferroptosis and thereby promoting cell survival (Fig. 6).

Figure 6 Proposed schematic diagram of the mechanism by which the LUCAT1/miR-34a-5p/GCH1 axis regulates ferroptosis in lung cancer cells. RSL3, RAS-selective lethal 3; GPX4, glutathione peroxidase 4; LUCAT1, lung cancer-associated transcript 1; GCH1, GTP cyclohydrolase 1; miR, microRNA.

Ferroptosis-associated proteins have been extensively reported, some of which have been targeted to increase the sensitivity of cancer to certain drugs (58). For instance, Mao et al (7) reported that DHODH is a ferroptosis suppressor, and the DHODH inhibitor, brequinar, selectively inhibited the growth of tumours with low GPX4 expression by inducing ferroptosis. In addition, the growth of the tumours with high GPX4 expression was suppressed by inducing ferroptosis following combined treatment with brequinar and the FDA-approved drug, sulfasalazine. In the present study, GADD45A, EPGN, HBEGF and TGFA were identified as potentially novel ferroptosis-related genes by analysing their mRNA expression profiles under ferroptosis conditions. Qi et al (59) also reported that sodium selenite inhibited cervical cancer by activating GADD45A in a ROS-dependent manner (56), which is essential for ferroptosis. Although lncRNAs are vital in biological processes, data on ferroptosis-related lncRNAs remains limited. In the present study, the analysis of mRNA and lncRNA expression profiles suggested the involvement of LUCAT1 in ferroptosis. The findings indicated that LUCAT1 was significantly upregulated in A549 cells treated with RSL3, which was prevented by Fer-1. Previous studies have reported LUCAT1 upregulation in Erastinor sorafenib-treated liver cancer cells and in pancreatic adenocarcinoma cells subjected to cystine depletion (a ferroptosis-inducing condition) (34,35,60,61). Moreover, nuclear factor erythroid 2-related factor 2 (62), specificity protein 1 (63), signal transducer and activator of transcription 3 (64) and E74-like ETS transcription factor 1 (65) regulate LUCAT1 transcription; however, whether RSL3 controls these transcription factors to regulate LUCAT1 expression requires further exploration.

Previous reports have shown that LUCAT1 promotes tumour cell migration, invasion (63) and metastasis (66), is linked to worse outcomes (67) and has been described as a key regulator of stemness in cancer (68). Vierbuchen et al (69) found that LUCAT1 levels are elevated in a large cohort of patients with inflammatory disorders, underlining its key role in immune regulation. Tao et al (70) demonstrated that LUCAT1 prevents stem cell apoptosis and improves the efficacy of stem cell therapy for ischaemic cardiovascular disease. Reduced LUCAT1 expression promotes G0/G1 phase block of the cell cycle in several tumour cells (71-75), while high expression leads to the development of cellular resistance, for instance cisplatin resistance in non-small cell lung cancer (NSCLC) (76), methotrexate resistance in osteosarcoma (77) and camptothecin, 5-fluorouracil, adriamycin and oxaliplatin resistance in colorectal cancer (22). In the present study, LUCAT1 knockdown increased the sensitivity of lung cancer cells to ferroptosis, indicating that this method could be a novel approach to treating lung cancer.

In addition, in the present study, knocking down LUCAT1 expression inhibited the expression of GCH1, which promotes tumorigenesis in KRAS-driven lung cancer (78) and was identified as a ferroptosis suppressor in a genome-wide activation screen (6). The localisations of lncRNAs are closely associated with their functions (79). In the present study, LUCAT1 was predicted to be localised in the cytoplasm, where it may act as a miR-34a-5p sponge. miR-34a-5p has been shown to inhibit cell proliferation in lung adenocarcinoma (80) and is a driver in CdCl2-induced ferroptosis (81). The findings of the present study demonstrated that miR-34a-5p inhibited GCH1 expression, which in turn promoted ferroptosis in the presence of RSL3 in lung cancer.

Although this study provides a new theoretical basis for the treatment of lung cancer, there are still some limitations. Specifically, the findings of the present study suggested that the ferroptosis inducer, RSL3, regulated LUCAT1, yet the precise regulatory mechanism remains unclear. The expression levels of LUCAT1 in lung cancer and normal tissues were determined through bioinformatics analysis; however, the lack of available clinical samples hinders further validation. Furthermore, in vivo experiments are necessary to substantiate the suppressive function of LUCAT1 in ferroptosis. Consequently, additional research is warranted to address these unresolved questions.

In summary, the results of the present study suggested that LUCAT1 may have a critical role in ferroptosis. Overexpression of LUCAT1 induced ferroptosis resistance to rescue cell survival in the presence of RSL3. By contrast, knocking down LUCAT1 expression enhanced susceptibility to RSL3- and Erastin-induced ferroptosis via miR-34a-5p-mediated GCH1 downregulation in NSCLC cells. Therefore, the results of the present study identified a novel pathway, the LUCAT1/miR-34a-5p/GCH1 axis, which regulated ferroptosis sensitivity and may be targeted for the treatment of lung cancer.

Supplementary Data

Availability of data and materials

The RNA sequencing data generated in the present study may be found in the GEO database under accession number GSE247883 or at the following URL: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE247883. All other data generated in the present study may be requested from the corresponding author.

Authors' contributions

XZ, FX, HF and FT conceived and designed the experiments. FT, RZ, KD, YZ, HY, HL, QW and ZC performed the experiments. FT and CG analysed the data. FT wrote the manuscript. XZ, FX, HF and CG revised the manuscript. XZ and FT confirm the authenticity of all the raw data. All authors read and approved the final version of the manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Abbreviations:

Acknowledgments

Not applicable.

Funding

The present study was supported by The National Natural Science Foundation of China (grant nos. 82073489 and 32071290).

References